Signal compression method for biomedical image using the discrete orthogonal Gauss-Hermite transform

Authors:
P. Lazaridis;G. Debarge;P. Gallion;Z. Zaharis;D. Kampitaki;A. Hatzigaidas;A. Papastergiou;G. Grammatikopoulos
Affiliations:
Department of Electronics, Alexander Technological Educational Institute of Thessaloniki, EPEAEK II, Thessaloniki, Macedonia, Greece;Département Communications et Electronique, Unité de Recherche Associée au Centre National de la Recherche Scientifique, Nationale Supérieure des Télécommunications, ...;Department of Electronics, Alexander Technological Educational Institute of Thessaloniki, EPEAEK II, Thessaloniki, Macedonia, Greece;Department of Electronics, Alexander Technological Educational Institute of Thessaloniki, EPEAEK II, Thessaloniki, Macedonia, Greece;Department of Electronics, Alexander Technological Educational Institute of Thessaloniki, EPEAEK II, Thessaloniki, Macedonia, Greece;Department of Electronics, Alexander Technological Educational Institute of Thessaloniki, EPEAEK II, Thessaloniki, Macedonia, Greece;Department of Electronics, Alexander Technological Educational Institute of Thessaloniki, EPEAEK II, Thessaloniki, Macedonia, Greece;Department of Esthetics, Alexander Technological Educational Institute of Thessaloniki, EPEAEK II, Thessaloniki, Macedonia, Greece
Venue:
ISCGAV'06 Proceedings of the 6th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Year:
2006

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Abstract

A method is presented for the compression of biomedical images using in place of the discrete cosine transform (DCT) the discrete orthogonal Gauss-Hermite transform (DOGHT). The latter expands the signals on a basis of Gauss-Hermite functions instead of the cosine functions and leads, in many practical cases, to 2-3 times better compression for the same reconstruction error as the DCT. This is achieved because the DOGHT transform of this paper combines the advantages of the DCT transform and the advantages of the transforms, which are based on wavelet expansions.